[Gretl-users] intreg doubt

Hi everyone: I´d like to compare results of censored model (tobit) with OLS regression. For this, I´ve prepared the following hypothetical data, that I´ve copied below. The censoring point for y is 1000. I´d estimated the following models: ols Y const X ols YCENS const X And I´d like to estimate intreg with upper limit of 1000 (equivalent to Stata´s command: tobit YCENS X, ul(1000)). But, I´ve problems to define the variables minvar and maxvar. The model do not converge with Y_cens_minvar and Ycens_maxvar and X as independent variable. Are they correct? Additional question: Is there any equivalent to Stata´s command: truncreg YTRUNC XTRUNC, ul(1000)? Thanks in advance for any help. Maria Dolores Montoya Diaz The database is: Y X YCENS YTRUNC XTRUNC Ycens_minvar Ycens_maxvar 1 150 2 150 150 2 150 2 150 4 150 150 4 150 3 160 4 160 160 4 160 4 192 4 192 192 4 192 5 230.4 4 230.4 230.4 4 230.4 6 276.48 3 276.48 276.48 3 276.48 7 331.776 3 331.776 331.776 3 331.776 8 398.131 4 398.131 398.1312 4 398.1312 9 477.757 4 477.757 477.7574 4 477.7574 10 573.309 4 573.309 573.3089 4 573.3089 11 687.971 6 687.971 687.9707 6 687.9707 12 825.565 6 825.565 825.5649 6 825.5649 13 990.678 6 990.678 990.6778 6 990.6778 14 1188.813 8 1000 1000 15 1426.576 8 1000 1000 16 1711.891 8 1000 1000 17 1700 8 1000 1000 18 1700 8 1000 1000 19 1500 7 1000 1000 20 1200 6 1000 1000 21 1440 7 1000 1000 22 1728 8 1000 1000 23 2073.6 8 1000 1000 24 2488.32 8 1000 1000 25 2985.984 8 1000 1000 26 3583.181 18 1000 1000 27 4299.817 18 1000 1000 28 210 4 210 210 4 210 29 210 4 210 210 4 210 30 224 4 224 224 4 224 31 268.8 4 268.8 268.8 4 268.8 32 322.56 4 322.56 322.56 4 322.56 33 387.072 6 387.072 387.072 6 387.072 34 464.486 7 464.486 464.4864 7 464.4864 35 557.384 8 557.384 557.3837 8 557.3837 36 668.86 9 668.86 668.8604 9 668.8604 37 802.632 11 802.632 802.6325 11 802.6325 38 963.159 11 963.159 963.159 11 963.159 39 1155.791 15 1000 1000 40 1386.949 15 1000 1000 41 1664.339 15 1000 1000 42 1997.207 15 1000 1000 43 2396.648 15 1000 1000 44 2380 15 1000 1000 45 2380 14 1000 1000 46 2100 15 1000 1000 47 1680 15 1000 1000 48 2016 15 1000 1000 49 2419.2 15 1000 1000 50 2903.04 16 1000 1000